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Chinese Journal of Applied Ecology ›› 2016, Vol. 27 ›› Issue (12): 3807-3815.doi: 10.13287/j.1001-9332.201612.021

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Hyperspectral inversion of soil water and salt content in soils with different textures

LI Ya-li, QIAO Jiang-fei, DONG Tian-yu, WANG Hai-jiang*   

  1. Key Laboratory of Oasis Ecology Agriculture of Xinjiang Bingtuan, Shihezi University, Shihezi 832003, Xinjiang, China
  • Received:2016-04-12 Online:2016-12-18 Published:2016-12-18
  • Contact: * E-mail: whj-219@163.com
  • Supported by:
    This work was supported by the International Science and Technology Cooperation Project (2015DFA11660), the Corps Science and Technology Project (2014AB002) and the Shihezi University Rroject (gxjs2012-zdgg03-02, RCZX201522).

Abstract: In order to monitor soil water and salt content of saline soil conveniently and quickly, this paper took the typical salinization irrigation district of Xinjiang as the research object, obtained the spectral curve of soil water and salt content by using portable spectrometers based on the hyperspectral technology, transformed the original spectra of soil using the first order differential, second order differential and continuum removal methods. The results showed that the transformation of the original spectral data was beneficial to fingerprint band extraction of soil properties, and the method was not same in soils with different textures. In loam soil, continuum removal analysis was the best method for extraction of characteristic bands when the soil water content was 0% and 10%, first order differential equations were the best method when the soil water content was 15%, and second order differential equations were the best method when the soil water content was 19%. In sandy soil, continuum removal analysis was the best method for extraction of characteristic bands when the soil water content was 0%, whereas second order differential equations were the best method when soil water content was 10%, 15% or 19%. The transformed data were screened for inversion models of soil water and salt content by using the partial least squares regression method. When thesalinity was < 6.38 mS·cm-1 in loam soil and < 5.94 mS·cm-1 in sandy soil, the decision coefficients (Rcal2), internal cross validation (Rcv2), and external validation (Rval2) were greater than 0.65 (P<0.05). When the soil moisture content was less than 16% in loam soil and 12% in sandy soil, the inversion accuracy of model was higher. The results would provide a reference threshold for si-multaneously monitoring soil water and salt content in salinized areas.